方法证据记录
Emotion Detection
Emotion detection is a natural-language-processing task that classifies the basic and complex emotions expressed in text — fear, joy, anger, sadness, surprise, and disgust — within a recognised emotion framework such as Ekman's basic-emotions model or Plutchik's wheel. It builds on Paul Ekman's 1992 argument for a small set of universal basic emotions, going beyond a simple positive/negative split to attach a specific emotion label to each piece of text.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Emotion Detection in Text
分类方法记录 · process-pipeline / text-mining
- Ekman, P. (1992). An Argument for Basic Emotions. Cognition & Emotion, 6(3-4), 169-200. · DOI 10.1080/02699939208411068
- Mohammad, S.M. & Turney, P.D. (2013). Crowdsourcing a Word–Emotion Association Lexicon. Computational Intelligence, 29(3), 436-465. · DOI 10.1111/j.1467-8640.2012.00460.x
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